It is known to use acceleration and/or vibration sensors attached to bearing rings of rolling bearings to detect defects in the rolling elements or on the raceways. The sensor data are processed either in a data processing unit integrated in the bearing or attached to the bearing or to its housing or alternatively in a remote monitoring unit.
The bearing condition monitoring technology has been originally conceived for large-size bearings for use e.g. in wind turbines or trains. The scope of applicability of the bearing condition monitoring technology now continuously expands toward bearings of smaller size, e.g. for use in trucks or automobiles and will further expand due to the progress of miniaturization.
Many approaches have been considered ranging from the simple, quantitative rule-based to self-learning neural-network, feature extraction and historically calibrated methods such as novelty detection. However many of the advanced or “smart” methods, though impressive from an academic viewpoint, are lacking in cross industry field experience and the complexity results in bars to the industrial implementation.
In most cases, the methods involve one or more of the measurement tools employing an enveloped (demodulated) vibration signal. These methods include quantitative methods such as detection of the overall amplitude, quantitative statistics (RMS, SD, Variance), counts, periodicity, use of the autocorrelation properties, Hilbert space analysis or Cyclic Time Analysis (CTA). Further known methods include qualitative methods such as characteristic statistics (kurtosis, CF etc.), or methods based on contribution (CTA) fraction or harmonic content fraction.
A problem that affects Condition Monitoring (CM) across all types of machines is at what level Alert and Alarm (Amber and Red) thresholds should be configured for reliable bearing defect detection, in particular when utilizing Acceleration Enveloping measurements. Any method that utilizes the higher frequency ranges of vibration for quantitative (absolute amplitude) assessment of bearing damage severity is plagued with many factors that can significantly amplify or attenuate the amplitudes observed. Hence within these higher frequency ranges the use of fixed quantitative Alert and Alarm thresholds across a range of different applications or even similar applications is unworkable, so individual thresholds are necessary which require many man-hours of an analysts time to fine tune these thresholds from sensor to sensor and from machine to machine.
In the case of rail axle bearing monitoring, the defect frequency amplitudes which are indicative of a bearing with a significant defect can be seen to vary from tenths of a gE to over 10 gE depending on bearing type, axle-box design, sensor location and mounting and sensor orientation. In such rail applications, a common measurement technology is the wireless sensor node for which, when fitted as an aftermarket solution, it is often not feasible to obtain the ideal location and orientation. Also it can be expected that such a RAG (Red Amber Green) method is incorporated into the wireless node.
Vibration measurements collected from rail axle bearings often include a significant amount of external noise, much of which appears to be related to wheel-to-rail interaction which can vary significantly across the different rail applications, tracks, wheel-sets and measurement nodes (inconsistent transfer function). Such external noises often cause an increase in False Positives when using “quantitative” severity methods and thresholds. Whilst “relative” methods with similar external noises would produce an increase in False Negatives. From a CM analyst's point of view or an auto-diagnostic system approach it is preferable to have fewer False Positives even though that might result in a few more False Negatives and often there is a far more clearer separation between CI values when a defect is present than when one is not.
However, up to now, none of these methods achieves the reliability of experienced human engineers looking at the spectrum to detect a defect based on a sequence of harmonics to assess the severity thereof based on the end user risk specifications.